• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.3
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• pp.256-262
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• 1998

The torque of SRM depends on phase current and the derivative of inductance. But the inductance of SRM is nonlinearly changed according to rotor position angle and phase current because of saturation in magnetic circuit, and it is difficult to control the desired torque. This paper proposes inductance modeling method using ANN(Artificial Neural Network) that is used to simulate the inductance which is nonlinearly varied with rotor position and current. The torque ripple is analyzed and input voltage and current condition to reduce torque ripple is simulated by inductance model.

• 한국터널공학회:학술대회논문집
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• 2005.04a
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• pp.105-116
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• 2005

This study investigated the applicability of the Artificial Neural Network(ANN) technique for prediction of tunnel behavior. For training data collection, a series of finite element analyses were conducted for actual tunnel project site. Using the data, optimimzed ANNs were developed through a sensitivity study on internal parameters. The developed ANNs can make tunneling related predictions such as tunnel crown settlement, shotcrete lining stress, ground surface settlement, and groundwater inflow rate. The results indicated that the developed ANNs can be used as an effective and efficient tool for tunnelling related prediction in practical tunneling situations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.271-272
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• 2020

Data mining is known useful in medical area when no availability of evidence favoring a particular treatment option is found. Huge volume of structured/unstructured data is collected by the healthcare field in order to find unknown information or knowledge for effective diagnosis and clinical decision making. The data of 5,179 records considered for analysis has been collected from Korean National Health and Nutrition Examination Survey(KHANES) during 2-years. Data splitting, referred as the training and test sets, was applied to predict to fit the model. We analyzed to predict chronic kidney disease (CKD) using data mining method such as naive Bayes, logistic regression, CART and artificial neural network(ANN). This result present to select significant features and data mining techniques for the lifestyle factors related CKD.

• Computers and Concrete
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• v.33 no.6
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• pp.739-754
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• 2024

This study presents an innovative AI-driven approach to assess the ultimate axial load in Double-Skinned Profiled Steel sheet Composite Walls (DPSCWs). Utilizing a dataset of 80 entries, seven input parameters were employed, and various AI techniques, including Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Decision Tree with AdaBoost Regression, Random Forest Regression, Gradient Boost Regression Tree, Elastic Net Regression, Ridge Regression, and LASSO Regression, were evaluated. Decision Tree Regression and Random Forest Regression emerged as the most accurate models. The top three performing models were integrated into a hybrid approach, excelling in accurately estimating DPSCWs' ultimate axial load. This adaptable hybrid model outperforms traditional methods, reducing errors in complex scenarios. The validated Artificial Neural Network (ANN) model showcases less than 1% error, enhancing reliability. Correlation analysis highlights robust predictions, emphasizing the importance of steel sheet thickness. The study contributes insights for predicting DPSCW strength in civil engineering, suggesting optimization and database expansion. The research advances precise load capacity estimation, empowering engineers to enhance construction safety and explore further machine learning applications in structural engineering.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1230-1232
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• 1999

In this research, ANN load model was built on results of field test using residential load, and then proposed ANN load model was applied to transient analysis. The results of this research are as follows. The first, component load modeling using ANN was implemented. The second, group load model was proposed by aggregation of component load. The third, proposed load model was applied to power system analysis. Therefore, Importance of load modeling and precise load modeling method was suggested in this paper.

• Transactions of Materials Processing
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• v.17 no.8
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• pp.619-626
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• 2008

In this study, design methodology to determine optimal shape of the anvil in sizing press process has been proposed to minimize crop length of the AISI 1010 slab in horizontal rolling after width reduction. Shape of anvil were selected to 12 cases by design of experiment, and the dog-bone shapes and the crop length were determined by FE-analysis. Also, the anvil shape, which has minimum crop length, were determined by artificial neural network(ANN). As a result of FE-analysis, it can be seen that the crop length was increased with increasing center thickness in the dog-bone shape after width reduction. The anvil shape which has minimum crop length, was estimated to ${\theta}_{1}=21^{\circ}{\theta}_{2}=14^{\circ}$ by FE-analysis and ANN.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1C
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• pp.63-72
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• 2006

Due to rapid development of many cities in Korea, many public facilities are required to be built as well as complementary civil structures. Consequently, a number of tunnel constructions are currently carried out throughout the country, and many more tunnels are planned to be constructed in the near future. Tunnel excavation in a city often causes serious damage to above-ground structures and sewer system because of unexpected settlement. In order to prevent the destruction, the tunnel, which bypasses the center of a city, must be specially evaluated for its influence to other structure. In addition, since a slight disturbance of above-ground structure causes numerous public complaints and civil appeals, it must be approached with different method than the mountain tunnels. In this paper, the evaluation method using the Artificial Neural Network (ANN) has been studied. The method begins with an analysis of the minimal sectional area. If its result can be used to approximate the general influence of the whole section, the actual evaluation using ANN will take off. In addition, it also studies the construction management method which reflects the real time soil behavior and environment influence during construction using Geographic Information System (GIS).

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.155-157
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.